Update README.md

README.md

@@ -10,10 +10,9 @@ license: apache-2.0
 
 
 <p align="center">
-<b>Task-oriented finetuning for better embeddings on neural search</b>
+<b>The text embedding suit trained by [Jina AI](https://github.com/jina-ai), [Finetuner team](https://github.com/jina-ai/finetuner).</b>
 </p>
 
-The text embedding suit trained by [Jina AI](https://github.com/jina-ai), [Finetuner team](https://github.com/jina-ai/finetuner).
 
 ## Intented Usage & Model Info
 
@@ -28,10 +27,10 @@ With a compact size of just 35 million parameters,
 the model enables lightning-fast inference while still delivering impressive performance.
 Additionally, we provide the following options:
 
-- jina-embedding-b-en-v1: 110 million parameters.
-- jina-embedding-l-en-v1: 800 million parameters.
-- jina-embedding-xl-en-v1: 3 billion parameters.
-- jina-embedding-xxl-en-v1: 11 billion parameters.
+- `jina-embedding-b-en-v1`: 110 million parameters.
+- `jina-embedding-l-en-v1`: 800 million parameters.
+- `jina-embedding-xl-en-v1`: 3 billion parameters.
+- `jina-embedding-xxl-en-v1`: 11 billion parameters.
 
 ## Data & Parameters
 
@@ -39,4 +38,25 @@ More info will be released together with the technique report.
 
 ## Metrics
 
-## Usage
+We compared the model against `all-minilm-l6-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
+
+|FIELD1                        |STS12|STS13|STS14|STS15|STS16|STS17|TRECOVID|Quora|SciFact|param    |context length|
+|------------------------------|-----|-----|-----|-----|-----|-----|--------|-----|-------|---------|------|
+|all-minilm-l6-v2              |0.724|0.806|0.756|0.854|0.79 |0.876|0.473   |0.876|0.645  |33m      |256|
+|all-mpnet--base-v2            |0.726|0.835|0.78 |0.857|0.8  |0.906|0.513   |0.875|0.656  |110m     |256|
+|ada-embedding-002             |0.698|0.833|0.761|0.861|0.86 |0.903|0.685   |0.876|0.726  |Unknown  |8024|
+|jina-embedding-small          |0.738|0.781|0.732|0.833|0.785|0.859|0.471   |0.852|0.567  |35m      |512|
+
+For more tasks and metrics, please checkout [MTEB](https://huggingface.co/spaces/mteb/leaderboard) benchmark.
+
+## Usage
+
+```python
+!pip install finetuner[text]
+
+import finetuner
+
+
+model = finetuner.get_model('jinaai/jina-embedding-s-en-v1')
+embeddings = model.encode(['sentence 1', 'sentence 2'])
+```